A number of application domains make use of text documents in their work process. For example, radiologists routinely review medical images and document their work in the form of free-text reports. Although these reports may have a number of standardized sections, the content of the sections contains natural language. Frequently, radiologists incorporate explicit references to image data in their reports. When reading the report, it may take time and/or effort to look up the referenced image data.
“Medical-Image Retrieval Based on Knowledge-Assisted Text and Image Indexing”, by C. Lacoste et al., in IEEE Transactions on Circuits and Systems for Video Technology, Vol. 17, No. 7, pp. 889-900, July 2007, discloses to facilitate automatic indexing and retrieval of large medical-image databases. Both images and associated texts are indexed using medical concepts from the Unified Medical Language System (UMLS) meta-thesaurus.
“Automatic semantic indexing of medical images using a web ontology language for case-based image retrieval”, by G. Allampalli-Nagaraj et al., in Engineering Applications of Artificial Intelligence 22 (2009) pp. 18-25, discloses a system implemented to evaluate the retrieval efficiency of images when they are semantically indexed using a combination of a web ontology language and the low-level features of the image.